A Privacy Review of Vertically Partitioned Data-based PPCF Schemes

Murat Okkalioglu; Mehmet Koc; Huseyin Polat

A Privacy Review of Vertically Partitioned Data-based PPCF Schemes

Abstract

E-commerce companies utilize collaborative filtering approaches to provide recommendation in order to attract customers. Consumer participation through supplying feedback is an important component for a recommendation system to produce accurate predictions. New companies in the marketplace might lack enough data for collaborative filtering purposes. Thus, companies can come together to share their horizontally or vertically partitioned data for better services. Although partitioned data-based recommendation schemes provide accurate predictions, privacy issues might pose different risks to the companies participating into such collaboration. Privacy-preserving collaborative filtering schemes aim to provide accurate predictions without neglecting the privacy of such data holders. However, the collaborating partiesâ€™ privacy might not be protected as much as believed provided by these schemes. In this study, the privacy, offered by vertically partitioned binary ratings-based privacy-preserving collaborative filtering schemes, is examined by three different attacks and experimentally tested. Empirical outcomes show that the collaborating parties are still able to derive their confidential data.

Keywords

Privacy, Collaborative filtering, Binary ratings, Vertically partitioned data, Attack scenarios

References

J. Jacoby, “Information Load and Decision Quality: Some Contested Issues,” J. Mark. Res., Vol. 14, No. 4, pp. 569-573, 1977.
J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, San Fransisco, CA, USA, 43-52, 1998.
J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Inf. Syst., Vol. 22, No. 1, pp. 5- 53, 2004.
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry,” Commun. ACM, Vol. 35, No. 12, pp. 61-70,
X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., Vol. 2009, pp. 4:2-4:2, 2009.
A. Bilge, C. Kaleli, I. Yakut, I. Gunes, and H. Polat, “A Survey of Privacy-Preserving Collaborative Filtering Schemes,” Int. J. Softw. Eng. Knowl. Eng., Vol. 23, No. 08, pp. 1085-1108, 2013.
J. Canny, “Collaborative Filtering with Privacy”, in Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, USA, 45-57, 2002.
L. F. Cranor, “‘I Didn’t Buy it for Myself’”, in Proceedings of the ACM Workshop on Privacy in the Electronic Society, Washington, DC, USA, 111-117, 2003.

J. Phelps, G. Nowak, and E. Ferrell, “Privacy Concerns and Consumer Willingness to Provide Personal Information,” J. Public Policy Mark., Vol. 19, No. 1, pp. pp. 27-41, 2000.
H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar, “On the Privacy Preserving Properties of Random Data Perturbation Techniques,” in Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, 99-106, 2003.
H. Dutta, H. Kargupta, S. Datta, and K. Sivakumar, “Analysis of Privacy Preserving Random Perturbation Techniques: Further Explorations,” in Proceedings of the ACM Workshop on Privacy in the Electronic Society, Washington, DC, USA, 31-38, 2003.
S. Zhang, J. Ford, and F. Makedon, “Deriving Private Information from Randomly Perturbed Ratings,” in Proceedings of the 6th SIAM International Conference on Data Mining, Bethesda, MD, USA, 59-69, 2006.
J. A. Calandrino, A. Kilzer, A. Narayanan, E. W. Felten, and V. Shmatikov, “``You Might Also Like:" Privacy Risks of Collaborative Filtering,” in Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, USA, 231-246, 2011.
M. Okkalioglu, M. Koc, and H. Polat, “On the Discovery of Fake Binary Ratings,” in Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain, 901-907, 2015.
H. Polat and W. Du, “Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques,” in Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, 625-628, 2003. [16]. H. Polat and W. Using Private Response Recommendations Techniques,” Lect. Notes Comput. Sci., Vol. 3918, pp. 637-646, 2006. Randomized
S. L. Warner, “Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias,” J. Am. Stat. Assoc., Vol. 60, No. 309, pp. 63-69, 1965.
M. Okkalioglu, M. Koc, and H. Polat, “On the Privacy of Horizontally Partitioned Binary Data-based Privacy- Preserving Collaborative Filtering,” Lect. Notes Comput. Sci., Vol. 9481, 2015.
H. Polat and W. Du, “Privacy-Preserving top-N Recommendation on Distributed Data,” J. Am. Soc. Inf. Sci. Technol., Vol. 59, No. 7, pp. 1093–1108, 2008.
C. Kaleli and H. Polat, “Providing Naïve Bayesian Classifier-based Private Recommendations on Partitioned Data,” Lect. Notes Comput. Sci., Vol. 4702, pp. 515-522, 2007.
C. Kaleli and H. Polat, “Providing Private Recommendations Using Naïve Bayesian Classifier,” Advances in Soft Computing, Vol. 43, pp. 168-173, 2007.
M. Tada, H. Kikuchi, and S. Puntheeranurak, “Privacy- Preserving Collaborative Filtering Protocol Based on Similarity between Items,” in Proceedings of 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, Australia, 573-578,
H. Polat and W. Du, “Privacy-Preserving top-N Recommendation on Horizontally Partitioned Data,” in Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Paris, France, 725-731,
H. Polat and W. Du, “Privacy-Preserving Collaborative Filtering on Vertically Partitioned Data” Lect. Notes Comput. Sci., Vol. 3721, pp. 651-658, 2005.
C. Kaleli and H. Polat, “Privacy-Preserving Naïve Bayesian Distributed Data,” Comput. Intell., Vol. 31, No. 1, pp. 47- 68, 2015. Recommendations on
C. Kaleli and H. Polat, “Privacy-Preserving SOM-based Recommendations on Horizontally Distributed Data,” Knowledge-Based Syst., Vol. 33, pp. 124-135, 2012.
C. Kaleli and H. Polat, “SOM-based Recommendations with Privacy on Multi-party Vertically Distributed Data,” Journal of the Operational Research Society, Vol. 63, No. 6, pp. 826-838, 2012.
S. Guo and X. Wu, “On the Use of Spectral Filtering for Privacy Preserving Data Mining,” in Proceedings of the ACM Symposium on Applied Computing, Dijon, France, 622-626, 2006.
S. Guo, X. Wu, and Y. Li, “Determining Error Bounds for Spectral Filtering based Reconstruction Methods in Privacy Preserving Data Mining,” Knowl. Inf. Syst., Vol. 17, No. 2, pp. 217-240, 2008.
Z. Huang, W. Du, and B. Chen, “Deriving Private Information from Randomized Data,” in Proceedings of the 24th ACM SIGMOD International Conference on Management of Data, Baltimore, MD, USA37-48, 2005.
M. Okkalioglu, M. Koc, and H. Polat, “Deriving Private Data in Vertically Partitioned Data-based PPCF Schemes,” in Proceedings of the 9th International Conference on Information Security and Cryptology, Ankara, Turkey, 1-7, 2015.
K. Miyahara and M. Pazzani, “Collaborative Filtering with the Simple Bayesian Classifier,” Lect. Notes Comput. Sci., Vol. 1886, pp. 679-689, 2000.

Details

Primary Language

English

Subjects

-

Journal Section

-

Authors

Murat Okkalioglu This is me

Mehmet Koc This is me

Huseyin Polat This is me

Publication Date

September 1, 2016

Submission Date

-

Acceptance Date

-

Published in Issue

Year 2016 Volume: 5 Number: 3

IZ

https://izlik.org/JA96HL83WP

Cite

RIS / Bibtex

APA

Okkalioglu, M., Koc, M., & Polat, H. (2016). A Privacy Review of Vertically Partitioned Data-based PPCF Schemes. International Journal of Information Security Science, 5(3), 51-68. https://izlik.org/JA96HL83WP

AMA

1.Okkalioglu M, Koc M, Polat H. A Privacy Review of Vertically Partitioned Data-based PPCF Schemes. IJISS. 2016;5(3):51-68. https://izlik.org/JA96HL83WP

Chicago

Okkalioglu, Murat, Mehmet Koc, and Huseyin Polat. 2016. “A Privacy Review of Vertically Partitioned Data-Based PPCF Schemes”. International Journal of Information Security Science 5 (3): 51-68. https://izlik.org/JA96HL83WP.

EndNote

Okkalioglu M, Koc M, Polat H (September 1, 2016) A Privacy Review of Vertically Partitioned Data-based PPCF Schemes. International Journal of Information Security Science 5 3 51–68.

IEEE

[1]M. Okkalioglu, M. Koc, and H. Polat, “A Privacy Review of Vertically Partitioned Data-based PPCF Schemes”, IJISS, vol. 5, no. 3, pp. 51–68, Sept. 2016, [Online]. Available: https://izlik.org/JA96HL83WP

ISNAD

Okkalioglu, Murat - Koc, Mehmet - Polat, Huseyin. “A Privacy Review of Vertically Partitioned Data-Based PPCF Schemes”. International Journal of Information Security Science 5/3 (September 1, 2016): 51-68. https://izlik.org/JA96HL83WP.

JAMA

1.Okkalioglu M, Koc M, Polat H. A Privacy Review of Vertically Partitioned Data-based PPCF Schemes. IJISS. 2016;5:51–68.

MLA

Okkalioglu, Murat, et al. “A Privacy Review of Vertically Partitioned Data-Based PPCF Schemes”. International Journal of Information Security Science, vol. 5, no. 3, Sept. 2016, pp. 51-68, https://izlik.org/JA96HL83WP.

Vancouver

1.Murat Okkalioglu, Mehmet Koc, Huseyin Polat. A Privacy Review of Vertically Partitioned Data-based PPCF Schemes. IJISS [Internet]. 2016 Sep. 1;5(3):51-68. Available from: https://izlik.org/JA96HL83WP